Complexity is the great challenge of 21st century biological research and pharmaceutical discovery. One approach to this challenge is the new field of computational cell biology, which has emerged at the confluence of cell biology, chemical kinetics, and computer science. Its potential lies in the development of new software tools that facilitate cell biological investigations even as the systems being studied grow increasingly complex. This Phase I SBIR proposal meshes database technology and kinetic modeling to construct one such tool. It aims to demonstrate the feasibility of a software platform that integrates database technology with modeling software. This platform will support the testing of multiple large-scale hypotheses against multiple experimental data sets. The first aim is to complete a prototype software application, called ProcessDB, with a robust set of features supporting efficient formulation, quantification, comparison, and testing of large models. ProcessDB is built on a database of cell and molecular biology primitives such as molecules, complexes, cellular locations, states, and processes, and will permit cell biologists to translate and export their diagrams, working hypotheses and experimental protocols to third-party modeling and optimization software for simulation and parameter estimation. The second aim of this project is feasibility testing. A panel of three expert cell and molecular biologists will exercise our software application by using it to build and test multiple working models of their biological systems. Feasibility will be demonstrated if the prototype software 1) has the stated features, 2) is available via the Internet, and 3) allows cell biologists to enter their theories easily and test them quantitatively against their own dynamic experimental data. No existing software package uses a database of molecules, complexes, places, and processes to manage the formulation and testing of multiple large-scale mechanistic models. The benefit to our users is a faster path to mechanistic understanding of complex cell biological pathways.